Ants have algorithms. If you think about an ant colony, it’s a computing device; there’s some wonderful work by Jean-Louis Deneubourg in Brussels and his collaborators that really started this field in a way with Ilya Prigogine and later on Jean Louis Deneubourg looking at the ways in which social insect colonies can interact. One example would be—it sounds trivial, but if you think about it, it is quite difficult—how can a colony decide between two food sources, one of which is slightly closer than the other? Do they have to measure this? Do they have to perform these computations?
We now know that this is not the case. Chris Langton and other researchers have also investigated these properties, whereby individuals just by virtue of the fact that one food source is closer, even if they are searching more or less at random, have a higher probability of returning to the nest more quickly. Which means they lay more chemical trail, which the other ants tend to follow. You have this competition between these sources. You have an interaction between positive feedback, which is the amplification of information—that’s the trail-laying behavior—and then you have negative feedback because of course if you just have positive feedback, there is no regulation, there is no homeostasis, you can’t create these accurate decisions.
There’s a negative feedback, which in this case is the decay of the pheromone, or the limited number of ants within the colony that you can recruit, and this delicate balance of positive and negative feedback allows the colony to collectively decide which source is closest and exploit that source, even though none of these individuals themselves have that knowledge.